Overview

Dataset statistics

Number of variables14
Number of observations534
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.7 KiB
Average record size in memory151.0 B

Variable types

Numeric8
Categorical6

Alerts

EXPERIENCE is highly overall correlated with AGE and 3 other fieldsHigh correlation
AGE is highly overall correlated with EXPERIENCE and 3 other fieldsHigh correlation
exp_div_edu is highly overall correlated with EXPERIENCE and 3 other fieldsHigh correlation
exp_div_age is highly overall correlated with EXPERIENCE and 3 other fieldsHigh correlation
exp_squared is highly overall correlated with EXPERIENCE and 3 other fieldsHigh correlation
EXPERIENCE has 11 (2.1%) zerosZeros
exp_div_edu has 11 (2.1%) zerosZeros
exp_div_age has 11 (2.1%) zerosZeros
exp_squared has 11 (2.1%) zerosZeros

Reproduction

Analysis started2023-10-02 20:08:42.042627
Analysis finished2023-10-02 20:08:45.263071
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WAGE
Real number (ℝ)

Distinct238
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0591886
Minimum0
Maximum3.7954892
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:45.472260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.252763
Q11.6582281
median2.0515563
Q32.4203681
95-th percentile2.9947318
Maximum3.7954892
Range3.7954892
Interquartile range (IQR)0.76214005

Descriptive statistics

Standard deviation0.52774223
Coefficient of variation (CV)0.25628649
Kurtosis-0.11709129
Mean2.0591886
Median Absolute Deviation (MAD)0.3811958
Skewness0.099748935
Sum1099.6067
Variance0.27851186
MonotonicityNot monotonic
2023-10-02T22:08:45.540859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.609437912 18
 
3.4%
2.302585093 18
 
3.4%
2.014903021 14
 
2.6%
1.504077397 12
 
2.2%
1.832581464 12
 
2.2%
1.208960346 12
 
2.2%
1.704748092 12
 
2.2%
1.945910149 12
 
2.2%
1.791759469 11
 
2.1%
2.079441542 11
 
2.1%
Other values (228) 402
75.3%
ValueCountFrequency (%)
0 1
 
0.2%
0.5596157879 1
 
0.2%
0.6981347221 1
 
0.2%
1.047318994 1
 
0.2%
1.098612289 2
 
0.4%
1.208960346 12
2.2%
1.223775432 2
 
0.4%
1.232560261 1
 
0.2%
1.238374231 1
 
0.2%
1.252762968 10
1.9%
ValueCountFrequency (%)
3.795489189 1
 
0.2%
3.269188639 1
 
0.2%
3.258096538 1
 
0.2%
3.218875825 1
 
0.2%
3.218075505 6
1.1%
3.146305132 1
 
0.2%
3.128075461 1
 
0.2%
3.113515309 4
0.7%
3.100092289 3
0.6%
3.091042453 1
 
0.2%

OCCUPATION
Real number (ℝ)

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1479401
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:45.596829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6558004
Coefficient of variation (CV)0.39918619
Kurtosis-0.92965189
Mean4.1479401
Median Absolute Deviation (MAD)1
Skewness-0.49087325
Sum2215
Variance2.7416749
MonotonicityNot monotonic
2023-10-02T22:08:45.646712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 156
29.2%
5 105
19.7%
3 97
18.2%
4 83
15.5%
1 55
 
10.3%
2 38
 
7.1%
ValueCountFrequency (%)
1 55
 
10.3%
2 38
 
7.1%
3 97
18.2%
4 83
15.5%
5 105
19.7%
6 156
29.2%
ValueCountFrequency (%)
6 156
29.2%
5 105
19.7%
4 83
15.5%
3 97
18.2%
2 38
 
7.1%
1 55
 
10.3%

SECTOR
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
0
411 
1
99 
2
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters534
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 411
77.0%
1 99
 
18.5%
2 24
 
4.5%

Length

2023-10-02T22:08:45.699689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T22:08:45.744495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 411
77.0%
1 99
 
18.5%
2 24
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 411
77.0%
1 99
 
18.5%
2 24
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 534
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 411
77.0%
1 99
 
18.5%
2 24
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 411
77.0%
1 99
 
18.5%
2 24
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 411
77.0%
1 99
 
18.5%
2 24
 
4.5%

UNION
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
0
438 
1
96 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters534
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 438
82.0%
1 96
 
18.0%

Length

2023-10-02T22:08:45.791886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T22:08:45.835146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 438
82.0%
1 96
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 438
82.0%
1 96
 
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 534
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 438
82.0%
1 96
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
Common 534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 438
82.0%
1 96
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 438
82.0%
1 96
 
18.0%

EDUCATION
Real number (ℝ)

Distinct17
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.018727
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:45.876027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.65
Q112
median12
Q315
95-th percentile18
Maximum18
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6153726
Coefficient of variation (CV)0.20089312
Kurtosis0.84077499
Mean13.018727
Median Absolute Deviation (MAD)1
Skewness-0.2036776
Sum6952
Variance6.840174
MonotonicityNot monotonic
2023-10-02T22:08:45.929063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
12 219
41.0%
16 71
 
13.3%
14 56
 
10.5%
13 37
 
6.9%
18 31
 
5.8%
11 27
 
5.1%
17 24
 
4.5%
10 17
 
3.2%
8 15
 
2.8%
15 13
 
2.4%
Other values (7) 24
 
4.5%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 3
 
0.6%
7 5
 
0.9%
8 15
2.8%
9 12
2.2%
10 17
3.2%
11 27
5.1%
ValueCountFrequency (%)
18 31
 
5.8%
17 24
 
4.5%
16 71
 
13.3%
15 13
 
2.4%
14 56
 
10.5%
13 37
 
6.9%
12 219
41.0%
11 27
 
5.1%
10 17
 
3.2%
9 12
 
2.2%

EXPERIENCE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.822097
Minimum0
Maximum55
Zeros11
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:45.989451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median15
Q326
95-th percentile42
Maximum55
Range55
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.37971
Coefficient of variation (CV)0.694627
Kurtosis-0.38094845
Mean17.822097
Median Absolute Deviation (MAD)8
Skewness0.68775794
Sum9517
Variance153.25722
MonotonicityNot monotonic
2023-10-02T22:08:46.052331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 28
 
5.2%
13 23
 
4.3%
10 23
 
4.3%
16 22
 
4.1%
8 19
 
3.6%
7 18
 
3.4%
12 18
 
3.4%
15 18
 
3.4%
3 18
 
3.4%
6 17
 
3.2%
Other values (42) 330
61.8%
ValueCountFrequency (%)
0 11
2.1%
1 12
2.2%
2 15
2.8%
3 18
3.4%
4 16
3.0%
5 15
2.8%
6 17
3.2%
7 18
3.4%
8 19
3.6%
9 15
2.8%
ValueCountFrequency (%)
55 1
 
0.2%
54 1
 
0.2%
49 1
 
0.2%
48 1
 
0.2%
47 2
 
0.4%
46 2
 
0.4%
45 6
1.1%
44 5
0.9%
43 7
1.3%
42 7
1.3%

AGE
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.833333
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:46.115496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q128
median35
Q344
95-th percentile60
Maximum64
Range46
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.726573
Coefficient of variation (CV)0.31836849
Kurtosis-0.58079326
Mean36.833333
Median Absolute Deviation (MAD)8
Skewness0.54829713
Sum19669
Variance137.51251
MonotonicityNot monotonic
2023-10-02T22:08:46.178616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
32 24
 
4.5%
26 22
 
4.1%
36 21
 
3.9%
34 20
 
3.7%
33 20
 
3.7%
35 18
 
3.4%
37 18
 
3.4%
29 18
 
3.4%
38 18
 
3.4%
28 18
 
3.4%
Other values (37) 337
63.1%
ValueCountFrequency (%)
18 4
 
0.7%
19 10
1.9%
20 14
2.6%
21 12
2.2%
22 13
2.4%
23 8
 
1.5%
24 13
2.4%
25 17
3.2%
26 22
4.1%
27 17
3.2%
ValueCountFrequency (%)
64 6
1.1%
63 5
0.9%
62 3
 
0.6%
61 9
1.7%
60 5
0.9%
59 3
 
0.6%
58 2
 
0.4%
57 9
1.7%
56 8
1.5%
55 9
1.7%

SEX
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
0
289 
1
245 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters534
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 289
54.1%
1 245
45.9%

Length

2023-10-02T22:08:46.235422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T22:08:46.278667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 289
54.1%
1 245
45.9%

Most occurring characters

ValueCountFrequency (%)
0 289
54.1%
1 245
45.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 534
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 289
54.1%
1 245
45.9%

Most occurring scripts

ValueCountFrequency (%)
Common 534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 289
54.1%
1 245
45.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 289
54.1%
1 245
45.9%

MARR
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
1
350 
0
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters534
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 350
65.5%
0 184
34.5%

Length

2023-10-02T22:08:46.325303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T22:08:46.368454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 350
65.5%
0 184
34.5%

Most occurring characters

ValueCountFrequency (%)
1 350
65.5%
0 184
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 534
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 350
65.5%
0 184
34.5%

Most occurring scripts

ValueCountFrequency (%)
Common 534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 350
65.5%
0 184
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 350
65.5%
0 184
34.5%

RACE
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
3
440 
1
67 
2
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters534
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 440
82.4%
1 67
 
12.5%
2 27
 
5.1%

Length

2023-10-02T22:08:46.414992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T22:08:46.459290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 440
82.4%
1 67
 
12.5%
2 27
 
5.1%

Most occurring characters

ValueCountFrequency (%)
3 440
82.4%
1 67
 
12.5%
2 27
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 534
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 440
82.4%
1 67
 
12.5%
2 27
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 440
82.4%
1 67
 
12.5%
2 27
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 440
82.4%
1 67
 
12.5%
2 27
 
5.1%

SOUTH
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
0
378 
1
156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters534
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 378
70.8%
1 156
29.2%

Length

2023-10-02T22:08:46.506688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T22:08:46.550054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 378
70.8%
1 156
29.2%

Most occurring characters

ValueCountFrequency (%)
0 378
70.8%
1 156
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 534
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 378
70.8%
1 156
29.2%

Most occurring scripts

ValueCountFrequency (%)
Common 534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 378
70.8%
1 156
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 378
70.8%
1 156
29.2%

exp_div_edu
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct208
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5589243
Minimum0
Maximum18.333333
Zeros11
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:46.601761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14285714
Q10.61538462
median1.1483516
Q32.065625
95-th percentile3.8116667
Maximum18.333333
Range18.333333
Interquartile range (IQR)1.4502404

Descriptive statistics

Standard deviation1.5933418
Coefficient of variation (CV)1.0220778
Kurtosis29.741516
Mean1.5589243
Median Absolute Deviation (MAD)0.68007972
Skewness3.951342
Sum832.46555
Variance2.5387382
MonotonicityNot monotonic
2023-10-02T22:08:46.667643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
3.7%
0.6666666667 15
 
2.8%
0 11
 
2.1%
0.25 11
 
2.1%
0.75 10
 
1.9%
1.166666667 9
 
1.7%
1.666666667 9
 
1.7%
1.333333333 9
 
1.7%
1.5 9
 
1.7%
2 9
 
1.7%
Other values (198) 422
79.0%
ValueCountFrequency (%)
0 11
2.1%
0.05882352941 1
 
0.2%
0.06666666667 1
 
0.2%
0.07142857143 2
 
0.4%
0.07692307692 3
 
0.6%
0.08333333333 5
0.9%
0.1176470588 1
 
0.2%
0.125 2
 
0.4%
0.1428571429 3
 
0.6%
0.1538461538 2
 
0.4%
ValueCountFrequency (%)
18.33333333 1
0.2%
13.5 1
0.2%
8.8 1
0.2%
8 1
0.2%
7.5 1
0.2%
7.166666667 1
0.2%
6.285714286 1
0.2%
6.142857143 1
0.2%
6.125 1
0.2%
6 1
0.2%

exp_div_age
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct219
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42786916
Minimum0
Maximum0.859375
Zeros11
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:46.734264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.090909091
Q10.3041958
median0.4375
Q30.58139535
95-th percentile0.71428571
Maximum0.859375
Range0.859375
Interquartile range (IQR)0.27719954

Descriptive statistics

Standard deviation0.19475922
Coefficient of variation (CV)0.45518405
Kurtosis-0.70595621
Mean0.42786916
Median Absolute Deviation (MAD)0.14132554
Skewness-0.24995942
Sum228.48213
Variance0.037931153
MonotonicityNot monotonic
2023-10-02T22:08:46.798200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 17
 
3.2%
0 11
 
2.1%
0.3076923077 11
 
2.1%
0.5 10
 
1.9%
0.4705882353 9
 
1.7%
0.4375 9
 
1.7%
0.5263157895 9
 
1.7%
0.4 8
 
1.5%
0.2173913043 7
 
1.3%
0.28 7
 
1.3%
Other values (209) 436
81.6%
ValueCountFrequency (%)
0 11
2.1%
0.04166666667 1
 
0.2%
0.04545454545 1
 
0.2%
0.04761904762 2
 
0.4%
0.05 3
 
0.6%
0.05263157895 5
0.9%
0.08 1
 
0.2%
0.08333333333 2
 
0.4%
0.09090909091 3
 
0.6%
0.09523809524 2
 
0.4%
ValueCountFrequency (%)
0.859375 1
0.2%
0.84375 1
0.2%
0.8 1
0.2%
0.7894736842 1
0.2%
0.7818181818 1
0.2%
0.7777777778 1
0.2%
0.7719298246 1
0.2%
0.7704918033 1
0.2%
0.7678571429 1
0.2%
0.7636363636 1
0.2%

exp_squared
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.59738
Minimum0
Maximum3025
Zeros11
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size24.5 KiB
2023-10-02T22:08:46.859481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q164
median225
Q3676
95-th percentile1764
Maximum3025
Range3025
Interquartile range (IQR)612

Descriptive statistics

Standard deviation570.2328
Coefficient of variation (CV)1.2117212
Kurtosis2.0998864
Mean470.59738
Median Absolute Deviation (MAD)200
Skewness1.6183486
Sum251299
Variance325165.45
MonotonicityNot monotonic
2023-10-02T22:08:46.924150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196 28
 
5.2%
169 23
 
4.3%
100 23
 
4.3%
256 22
 
4.1%
64 19
 
3.6%
49 18
 
3.4%
144 18
 
3.4%
225 18
 
3.4%
9 18
 
3.4%
36 17
 
3.2%
Other values (42) 330
61.8%
ValueCountFrequency (%)
0 11
2.1%
1 12
2.2%
4 15
2.8%
9 18
3.4%
16 16
3.0%
25 15
2.8%
36 17
3.2%
49 18
3.4%
64 19
3.6%
81 15
2.8%
ValueCountFrequency (%)
3025 1
 
0.2%
2916 1
 
0.2%
2401 1
 
0.2%
2304 1
 
0.2%
2209 2
 
0.4%
2116 2
 
0.4%
2025 6
1.1%
1936 5
0.9%
1849 7
1.3%
1764 7
1.3%

Interactions

2023-10-02T22:08:44.777038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.247794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.609076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.943079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.295977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.767799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.094362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.444727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.822311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.307006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.652785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.989414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.339458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.810286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.140018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.488453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.864424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.349686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.693213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.032203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.379959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.850126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.183003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.528631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.911276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.396472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.738164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.079407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.424937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.894199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.230443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.573391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.952826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.438596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.778948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.122178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.599928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.933716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.272854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.613775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.993383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.478961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.817584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.163608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.640493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.971238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.314164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.652657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:45.038338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.524431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.861612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.209653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.686442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.014390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.359280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.695859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:45.079434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.565346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:42.901060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.251637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:43.725968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.052946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.400957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-02T22:08:44.734532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-02T22:08:46.973297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WAGEOCCUPATIONEDUCATIONEXPERIENCEAGEexp_div_eduexp_div_ageexp_squaredSECTORUNIONSEXMARRRACESOUTH
WAGE1.0000.0090.3810.1690.2520.0890.1110.1690.0000.2240.2250.1440.0720.155
OCCUPATION0.0091.000-0.236-0.021-0.0710.0300.016-0.0210.3560.2190.4070.0530.0400.067
EDUCATION0.381-0.2361.000-0.306-0.107-0.475-0.429-0.3060.1800.0000.0000.0000.2260.103
EXPERIENCE0.169-0.021-0.3061.0000.9730.9760.9881.0000.1020.0690.1050.3530.0790.000
AGE0.252-0.071-0.1070.9731.0000.9050.9290.9730.0800.0370.1170.3420.0810.000
exp_div_edu0.0890.030-0.4750.9760.9051.0000.9980.9760.1350.0550.1190.1700.1590.047
exp_div_age0.1110.016-0.4290.9880.9290.9981.0000.9880.1290.0720.0280.3420.0870.091
exp_squared0.169-0.021-0.3061.0000.9730.9760.9881.0000.0870.0950.0690.2170.1030.130
SECTOR0.0000.3560.1800.1020.0800.1350.1290.0871.0000.0790.1710.0000.0000.055
UNION0.2240.2190.0000.0690.0370.0550.0720.0950.0791.0000.1460.0770.0640.068
SEX0.2250.4070.0000.1050.1170.1190.0280.0690.1710.1461.0000.0000.0000.000
MARR0.1440.0530.0000.3530.3420.1700.3420.2170.0000.0770.0001.0000.0000.000
RACE0.0720.0400.2260.0790.0810.1590.0870.1030.0000.0640.0000.0001.0000.110
SOUTH0.1550.0670.1030.0000.0000.0470.0910.1300.0550.0680.0000.0000.1101.000

Missing values

2023-10-02T22:08:45.143545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-02T22:08:45.228089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

WAGEOCCUPATIONSECTORUNIONEDUCATIONEXPERIENCEAGESEXMARRRACESOUTHexp_div_eduexp_div_ageexp_squared
ID
11.6292416108213511202.6250000.600000441
21.5993886109425711304.6666670.7368421764
31.8976206101211900300.0833330.0526321
41.3862946001242200300.3333330.18181816
52.01490360012173501301.4166670.485714289
62.5703206011392800300.6923080.32142981
71.49290460010274300312.7000000.627907729
82.9688756001292700300.7500000.33333381
92.58625961016113301300.6875000.333333121
102.1690546001292700300.7500000.33333381
WAGEOCCUPATIONSECTORUNIONEDUCATIONEXPERIENCEAGESEXMARRRACESOUTHexp_div_eduexp_div_ageexp_squared
ID
5251.7492005019344911113.7777780.6938781156
5262.03731750015113211300.7333330.343750121
5272.52572950015103100300.6666670.322581100
5282.77258950012123001311.0000000.400000144
5292.4672525011662810300.3750000.21428636
5302.4300985001852900300.2777780.17241425
5311.80828950012335111102.7500000.6470591089
5323.14630550117254811101.4705880.520833625
5332.98971450112133101311.0833330.419355169
5342.73306851016335501302.0625000.6000001089